DNA methylation remodeling in temozolomide resistant recurrent glioblastoma: comparing epigenetic dynamics in vitro and in vivo.

BACKGROUND

Glioblastoma is the most aggressive type of brain tumor and is associated with a poor prognosis. First-line treatment is surgical resection followed by radiotherapy and temozolomide-based chemotherapy. However, the duration of treatment with temozolomide is limited due to both its toxicity and the development of drug resistance. The prognostic and predictive factor for response to temozolomide is the methylation status of the MGMT promoter. Indeed, loss of MGMT promoter methylation is a major cause of chemoresistance. However, the development of drug resistance is not only associated with changes in MGMT methylation. The entire epigenome changes and acquires specific properties necessary for tumor progression.

METHODS

To study epigenetic alterations associated with temozolomide exposure, we generated a TMZ-resistant cell model. We investigated epigenetic alterations in the cell model and in a cohort of patients with recurrent glioblastoma using genome-wide methylome approaches (Epic Arrays 850 k).

RESULTS

We investigated the epigenetic changes associated with temozolomide exposure. Therefore, we generated a TMZ-resistant cell model and studied the epigenetic features acquired after selective temozolomide pressure. Our next step was to investigate the epigenomic differences between primary and recurrent tumors in a small cohort of patients. Finally, we performed a cross-analysis between the epigenetic differences found in TMZ-resistant cells and recurrent glioblastomas to identify common signatures that could be used to guide future resistance-overcoming studies.

CONCLUSIONS

Temozolomide induces significant epigenetic changes in glioblastoma, which may contribute to treatment resistance and increased tumor aggressiveness. The results suggest that further research into DNA methylation changes associated with TMZ resistance is crucial. The use of primary tumor cells in resistance models may help identify strategies to overcome chemoresistance in glioblastoma.